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Original article

Vol. 150 No. 1920 (2020)

Digital health and the COVID-19 epidemic: an assessment framework for apps from an epidemiological and legal perspective

  • Kerstin N. Vokinger
  • Vasileios Nittas
  • Claudia M. Witt
  • Sara Irina Fabrikant
  • Viktor von Wyl
DOI
https://doi.org/10.4414/smw.2020.20282
Cite this as:
Swiss Med Wkly. 2020;150:w20282
Published
17.05.2020

Summary

As COVID-19 spreads across the globe, crowdsourced digital technology harbours the potential to improve surveillance and epidemic control, primarily through increased information coverage, higher information speed, fast case tracking and improved proximity tracing. Targeting those aims, COVID-19-related smartphone and web-based health applications are continuously emerging, leading to a multitude of options, raising ethical and legal challenges and potentially overwhelming end users.

Building on an existing trustworthiness checklist for digital health applications, we searched the literature and developed a framework to guide the assessment of smartphone and web-based applications that aim to contribute to controlling the current epidemic or mitigating its effects. It further integrates epidemiological subject knowledge and a legal analysis, outlining the mechanisms through which new applications can support the fight against COVID-19.

The resulting framework includes 40 questions across 8 domains on “purpose”, “usability”, “information accuracy”, “organisational attributes / reputation”, “transparency”, “privacy” and “user control / self-determination”. All questions should be primarily answerable from publicly available data, as provided by application manufacturers. The framework aims to guide end users in choosing a transparent, safe and valuable application and suggests a set of information items that developers ideally make available to allow a balanced judgement and facilitate the trustworthiness of their products.

References

  1. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20(5):533–4. Published online February 23, 2020. doi:.https://doi.org/10.1016/S1473-3099(20)30120-1
  2. Salathé M, Althaus CL, Neher R, Stringhini S, Hodcroft E, Fellay J, et al. COVID-19 epidemic in Switzerland: on the importance of testing, contact tracing and isolation. Swiss Med Wkly. 2020;150:w20225. doi:.https://doi.org/10.4414/smw.2020.20225
  3. Leung GM, Leung K. Crowdsourcing data to mitigate epidemics. Lancet Digit Health. 2020;2(4):e156–7. doi:.https://doi.org/10.1016/S2589-7500(20)30055-8
  4. Sun K, Chen J, Viboud C. Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. Lancet Digit Health. 2020;2(4):e201–8. doi:.https://doi.org/10.1016/S2589-7500(20)30026-1
  5. Wakefield J. Coronavirus: Tracking app aims for one million downloads. BBC; 2020 [cited 2020 April 16]. Available from: https://www.bbc.com/news/technology-52033210.
  6. Buckee CO, Balsari S, Chan J, Crosas M, Dominici F, Gasser U, et al. Aggregated mobility data could help fight COVID-19. Science. 2020;368(6487):145–6. doi:.https://doi.org/10.1126/science.abb8021
  7. Richardson DB, Kwan M-P, Alter G, McKendry JE. Replication of scientific research: addressing geoprivacy, confidentiality, and data sharing challenges in geospatial research. Ann GIS. 2015;21(2):101–10. doi:.https://doi.org/10.1080/19475683.2015.1027792
  8. Kounadi O, Leitner M. Why does geoprivacy matter? The scientific publication of confidential data presented on maps. J Empir Res Hum Res Ethics. 2014;9(4):34–45. doi:.https://doi.org/10.1177/1556264614544103
  9. van Haasteren A, Gille F, Fadda M, Vayena E. Development of the mHealth App Trustworthiness checklist. Digit Health. 2019;5:2055207619886463. doi:.https://doi.org/10.1177/2055207619886463
  10. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. 2020;323(13):1239. doi:.https://doi.org/10.1001/jama.2020.2648
  11. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al.; China Medical Treatment Expert Group for Covid-19. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;382(18):1708–20. doi:.https://doi.org/10.1056/NEJMoa2002032
  12. Gengler I, Wang JC, Speth MM, Sedaghat AR. Sinonasal pathophysiology of SARS-CoV-2 and COVID-19: a systematic review of the current evidence. Laryngoscope Investig Otolaryngol. 2020:lio2.384. doi:.https://doi.org/10.1002/lio2.384
  13. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020;382(13):1199–207. doi:.https://doi.org/10.1056/NEJMoa2001316
  14. Du Z, Xu X, Wu Y, Wang L, Cowling BJ, Meyers LA. Serial Interval of COVID-19 among Publicly Reported Confirmed Cases. Emerg Infect Dis. 2020;26(6). doi:.https://doi.org/10.3201/eid2606.200357
  15. Nishiura H, Linton NM, Akhmetzhanov AR. Serial interval of novel coronavirus (COVID-19) infections. Int J Infect Dis. 2020;93:284–6. doi:.https://doi.org/10.1016/j.ijid.2020.02.060
  16. He X, Lau EHY, Wu P, Deng X, Wang J, Hao X, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med. 2020;26:672–5. doi:.https://doi.org/10.1038/s41591-020-0869-5
  17. Rothe C, Schunk M, Sothmann P, Bretzel G, Froeschl G, Wallrauch C, et al. Transmission of 2019-nCoV Infection from an Asymptomatic Contact in Germany. N Engl J Med. 2020;382(10):970–1. doi:.https://doi.org/10.1056/NEJMc2001468
  18. Kimball A, Hatfield KM, Arons M, James A, Taylor J, Spicer K, et al.; Public Health – Seattle & King County; CDC COVID-19 Investigation Team. Asymptomatic and Presymptomatic SARS-CoV-2 Infections in Residents of a Long-Term Care Skilled Nursing Facility - King County, Washington, March 2020. MMWR Morb Mortal Wkly Rep. 2020;69(13):377–81. Published online April 03, 2020. doi:.https://doi.org/10.15585/mmwr.mm6913e1
  19. Wei WE, Li Z, Chiew CJ, Yong SE, Toh MP, Lee VJ. Presymptomatic Transmission of SARS-CoV-2 - Singapore, January 23-March 16, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(14):411–5. Published online April 10, 2020. doi:.https://doi.org/10.15585/mmwr.mm6914e1
  20. Zou L, Ruan F, Huang M, Liang L, Huang H, Hong Z, et al. SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients. N Engl J Med. 2020;382(12):1177–9. doi:.https://doi.org/10.1056/NEJMc2001737
  21. Zhang S, Diao M, Yu W, Pei L, Lin Z, Chen D. Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: A data-driven analysis. Int J Infect Dis. 2020;93:201–4. doi:.https://doi.org/10.1016/j.ijid.2020.02.033
  22. Ferretti L, Wymant C, Kendall M, Zhao L, Nurtay A, Abeler-Dörner L, et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 2020;368(6491):eabb6936. doi:.https://doi.org/10.1126/science.abb6936
  23. Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the Asymptomatic Proportion of 2019 Novel Coronavirus onboard the Princess Cruises Ship, 2020. medRxiv. 2020:20025866. doi:.https://doi.org/10.1101/2020.02.20.20025866
  24. Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, et al.; Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health. 2020;8(4):e488–96. doi:.https://doi.org/10.1016/S2214-109X(20)30074-7
  25. Koppeschaar CE, Colizza V, Guerrisi C, Turbelin C, Duggan J, Edmunds WJ, et al. Influenzanet: Citizens Among 10 Countries Collaborating to Monitor Influenza in Europe. JMIR Public Health Surveill. 2017;3(3):e66. Published online September 21, 2017. doi:.https://doi.org/10.2196/publichealth.7429
  26. Guerrisi C, Turbelin C, Blanchon T, Hanslik T, Bonmarin I, Levy-Bruhl D, et al. Participatory Syndromic Surveillance of Influenza in Europe. J Infect Dis. 2016;214(suppl_4):S386–92. doi:.https://doi.org/10.1093/infdis/jiw280
  27. Milano B. How much access to data should be permitted during the COVID-19 pandemic? 2020 [16.04.2020]. Available from: https://today.law.harvard.edu/how-much-access-to-data-should-be-permitted-during-covid-19-pandemic/.
  28. Federal Data Protection and Information Officer. A Few Facts about the Federal Act on Data Protection 2020 [10.05.2020]. Available from: https://www.edoeb.admin.ch/edoeb/en/home/the-fdpic/legal-framework/ii--a-few-facts-about-the-federal-act-on-data-protection.html.
  29. Vokinger KN, Stekhoven DJ, Krauthammer M. Lost in Anonymization - A Data Anonymization Reference Classification Merging Legal and Technical Considerations. J Law Med Ethics. 2020;48(1):228–31. Published online April 29, 2020. doi:.https://doi.org/10.1177/1073110520917025
  30. Federal Data Protection and Information Officer. Latest News 2020 [10 May 2020]. Available from: https://www.edoeb.admin.ch/edoeb/en/home/latest-news/aktuell_news.html#-629760265.
  31. Swiss Confederation. Federal Act on Data Protection (FADP) 2019 [16 April 2020]. Available from: https://www.admin.ch/opc/en/classified-compilation/19920153/index.html.
  32. Rudin B. Das Recht auf Anonymität, Anonymität als Teil der informationellen Selbstbestimmung: wenig geregelte Anwendungsfälle und viel Handlungsbedarf. Digma. 2008;8:6–13.
  33. Rosenthal D, Jöhri Y. Handkommentar zum Datenschutzgesetz sowie weiteren, ausgewählten Bestimmungen. Zurich: Schulthess Juristische Medien AG; 2008.
  34. Maurer-Lambrou U, Gabor PB. Basler Kommentar, Datenschutzgesetz/Öffentlichkeitsgesetz. 3. Edition. Basel. 2014.
  35. Vokinger KN. Gesundheitsdaten im digitalen Zeitalter 2020 [16.04.2020]. Available from: https://jusletter.weblaw.ch/fr/juslissues/2020/1008/gesundheitsdaten-im-_bf12b7abee.html__ONCE&login=false.
  36. Vokinger KN. Digitale Bekämpfung von Covid-19 und die Rolle des Bundes(-rates). Eine rechtliche Würdigung des Einsatzes von Software. Submitted Manuscript. 2020.
  37. Pärli K, Baeriswyl B. Contact-Tracing: Nur die staatliche App schafft Vertrauen. Neue Zürcher Zeitung [Internet]. 2020 16.04.2020. Available from: https://www.nzz.ch/meinung/contact-tracing-nur-die-staatliche-app-schafft-vertrauen-ld.1550670?reduced=true.
  38. Albrecht UV, Malinka C, Long S, Raupach T, Hasenfuß G, von Jan U. Quality Principles of App Description Texts and Their Significance in Deciding to Use Health Apps as Assessed by Medical Students: Survey Study. JMIR Mhealth Uhealth. 2019;7(2):e13375. doi:.https://doi.org/10.2196/13375
  39. Albrecht UV. Transparency of health-apps for trust and decision making. J Med Internet Res. 2013;15(12):e277. doi:.https://doi.org/10.2196/jmir.2981
  40. Lewis TL. A systematic self-certification model for mobile medical apps. J Med Internet Res. 2013;15(4):e89. doi:.https://doi.org/10.2196/jmir.2446
  41. Albrecht U-V, Pramann O, von Jan U. Medical Apps – The Road To Trust. European Journal for Biomedical Informatics. 2015;11(3):7–12. doi:.https://doi.org/10.24105/ejbi.2015.11.3.3
  42. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. 2015;3(1):e27. doi:.https://doi.org/10.2196/mhealth.3422
  43. Wyatt JC, Thimbleby H, Rastall P, Hoogewerf J, Wooldridge D, Williams J. What makes a good clinical app? Introducing the RCP Health Informatics Unit checklist. Clin Med (Lond). 2015;15(6):519–21. doi:.https://doi.org/10.7861/clinmedicine.15-6-519
  44. Grundy QH, Wang Z, Bero LA. Challenges in Assessing Mobile Health App Quality: A Systematic Review of Prevalent and Innovative Methods. Am J Prev Med. 2016;51(6):1051–9. doi:.https://doi.org/10.1016/j.amepre.2016.07.009
  45. European Commission. What does the General Data Protection Regulation (GDPR) govern? [10.05.2020]. Available from: https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-does-general-data-protection-regulation-gdpr-govern_en.

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